Member of Technical Staff (Software Engineer, GPU Cluster Infrastructure)

Perplexity; Sacramento (CA); On-site; USD 200,000 - 300,000; Full time

Job summary

Perplexity is seeking an experienced platform engineer to own and evolve a self-serve compute platform for training and inference workloads on a GPU fleet. You will design systems to deploy and manage GPU resources across providers and build the Kubernetes-based orchestration that powers both long-running training jobs and production inference services.

The role requires deep Kubernetes expertise, experience with GPU hardware and networking, and the ability to coordinate across clouds.

Qualifications

• Deep Kubernetes experience with operators, CRDs, and multi-cluster federation.
• Experience managing GPU clusters at scale (NVIDIA hardware, CUDA, fast network).
• Orchestrated compute across multiple clouds (e.g., AWS, GCP) with understanding of cloud differences.
• Strong distributed-systems fundamentals: scheduling, allocation, and fault tolerance.

Responsibilities

• Build a self-serve compute platform for training and inference workloads.
• Design systems to launch training jobs and operate inference services without GPU provisioning overhead.
• Operate the GPU fleet and own provisioning, lifecycle, and capacity across providers.
• Develop scheduling and placement logic to maximize capacity and match workloads to hardware under constraints.
• Make failure boring by building fault tolerance, autoscaling, and observability across the fleet.
• Own Kubernetes orchestration for GPUs and write operators/CRDs; manage multiple clusters across providers.
• Set technical direction and collaborate with inference and cloud infra teams to shape platform architecture.

Skills

Kubernetes

GPU clusters

Multi-cloud

Distributed systems

Go/Rust/C++

End-to-end ownership

Tools

Kubernetes Operators

CRDs

Multi-cluster federation

NVIDIA CUDA

InfiniBand

RoCE

Job description

Perplexity serves hundreds of millions of queries a month, and every one of them fans out into multiple AI inference requests running in real time. Behind that sits a large GPU fleet spread across several cloud providers. Today, our inference engineers and researchers build models while also managing networking, securing capacity, and operating the underlying GPU clusters, responsibilities we want a dedicated platform team to own.

Your job is to take ownership of that infrastructure and hide its complexity behind a unified, self-serve platform for running training and inference workloads.

Responsibilities

• Build a self-serve compute platform.
• Design and own the systems that let inference engineers and researchers launch training jobs and operate inference services without managing GPU provisioning, cluster configuration, or provider-specific infrastructure.
• Operate the GPU fleet
• . Own provisioning, lifecycle management, reliability, and capacity integration across providers, giving teams a consistent way to use compute regardless of where it runs.
• Solve for GPU scarcity.
• Build the scheduling and placement logic that finds available capacity across providers, packs it efficiently, and gets the right workload onto the right hardware under real constraints.
• Support two very different workloads.
• Keep long-running distributed training jobs healthy while simultaneously guaranteeing the availability and latency of production inference services on the same fleet.
• Own the Kubernetes for GPU orchestration.
• Write the operators and CRDs, and manage many clusters across providers so the platform behaves the same everywhere we run.
• Make failure boring.
• Build the fault tolerance, autoscaling, and observability that keep the fleet utilized and let workloads survive node loss, provider hiccups, and capacity shifts without human intervention.
• Set technical direction across teams.
• Partner with inference and cloud infrastructure engineers to turn operational constraints into a coherent platform architecture and roadmap.

Qualifications

• We expect you to have real depth in most of these:
• Deep Kubernetes experience — custom operators, CRDs, and multi-cluster federation, not just running kubectl apply.
• You’ve managed GPU clusters at scale: NVIDIA hardware, CUDA, and the networking that makes them fast (InfiniBand or RoCE).
• You’ve orchestrated compute across multiple clouds (CoreWeave, AWS, GCP, or similar) and understand how different each one really is.
• Strong distributed systems fundamentals: scheduling, resource allocation, and fault tolerance under load.
• You write infrastructure and systems-level code in Go, Rust or C++.
• You’ve supported both long-running training jobs and high-availability inference services, and you know why they pull infrastructure in opposite directions.
• You own problems end-to-end and do well when the path forward isn’t laid out for you.

Additional experience we value

• Inference serving stacks: vLLM, SGLang, or TensorRT-LLM.
• Slurm or other HPC schedulers.
• GPU kernel work in CUDA or Triton — not required, but notable.
• High-speed interconnects: InfiniBand, RoCE, or RDMA in production.
• Observability for ML workloads: Prometheus, Grafana, or Weights & Biases.